IBM (international business machines) has published in its SPSS statistical software 2022 update a very important novel regression method entitled Kernel Ridge Regression (KRR). It is an extension of the currently available regression methods, and is suitable for pattern recognition in high dimensional data, particularly, when alternative methods fail. Its theoretical advantages are plenty and include the
- kernel trick for reduced arithmetic complexity,
- estimation of uncertainty by Gaussians unlike histograms,
- corrected data-overfit by ridge regularization,
- availability of 8 alternative kernel density models for datafit.
A very exciting and wide array of preliminary KRR research has already been published by major disciplines (like studies in quantum mechanics and nuclear physics, studies of molecular affinity / dynamics, atomisation energy studies, but also forecasting economics studies, IoT (internet of things) studies for e-networks, plant stress response studies, big data streaming studies, etc). In contrast, it is virtually unused in clinical research. This edition is the first textbook and tutorial of kernel ridge regressions for medical and healthcare students as well as recollection / update bench, and help desk for professionals. Each chapter can be studied as a standalone, and, using, real as well as hypothesized data, it tests the performance of the novel methodology against traditional regression analyses. Step by step analyses of over 20 data files stored at Supplementary Files at Springer Interlink are included for self-assessment. We should add that the authors are well qualified in their field. Professor Zwinderman is past-president of the International Society of Biostatistics (2012-2015) and Professor Cleophas is past-president of the American College of Angiology (2000-2002). From their expertise they should be able to make adequate selections of modern KRR methods for the benefit of physicians, students, and investigators. The authors have been working and publishing together for 24 years and their research can be characterized as a continued effort to demonstrate that clinical data analysis is not mathematics but rather a discipline at the interface of biology and mathematics.
Author(s): Ton J. Cleophas, Aeilko H. Zwinderman
Publisher: Springer
Year: 2022
Language: English
Pages: 277
City: Cham
Preface
Contents
Chapter 1: Traditional Kernel Regression
1.1 Summary
1.2 Introduction
1.3 Kernel Regression
1.4 Conclusion
1.5 References
Chapter 2: Kernel Ridge Regression (KRR)
2.1 Summary
2.2 History of Kernel Ridge Regression
2.3 Kernel Density Modeling
2.4 The Kernel Trick
2.5 Ridge Regularization
2.6 Conclusion
2.7 References
Chapter 3: Optimal Scaling vs Kernel Ridge Regression
3.1 Summary
3.1.1 Summaries of the Traditional Linear Regressions
3.1.2 Summaries of the Kernel Ridge Regressions
3.1.3 In Conclusion
3.2 Introduction
3.3 Optimal Scaling
3.4 Traditional Regressions
3.5 Kernel Ridge Regressions Scale 1
3.6 Kernel Ridge Regressions Scale 2
3.7 Kernel Ridge Regressions Scale 3
3.8 Conclusion
3.8.1 Summary of the Traditional R Square Values of the Scales 1-3 Models
3.8.2 Summary of Kernel Ridge Regressions (KRR)
3.9 References
Chapter 4: Examples of Published Kernel Ridge Regressions So Far
4.1 Summary
4.2 Introduction
4.3 History of Kernel Ridge Regression
4.4 A Brief Search of Kernel Ridge Regression Publications So Far
4.5 Courses Where the Upcoming Edition ``Kernel Ridge Regression in Clinical Research´´ Will Be Used
4.6 Conclusion
4.7 References
Chapter 5: Some Terminology
5.1 Summary
5.2 Alphabetical Enumeration
5.3 References
Chapter 6: Effect on Being Blind of Age/Sex Adjusted Mortality of Onchocerciasis Patients in 12,816 Personyears, Traditional v...
6.1 Summary
6.1.1 Summaries of Traditional Regressions
6.1.2 Summaries of Kernel Ridge Regressions
6.2 Introduction
6.3 Data Example
6.4 Traditional Linear Regression
6.5 Kernel Ridge Regressions
6.6 Conclusion
6.6.1 Summaries of Traditional Regressions
6.6.2 Summaries of Kernel Ridge Regressions
6.7 References
Chapter 7: Effect of Old Treatment on New Treatment, 35 Patients, Traditional Regressions vs Kernel Ridge Regressions
7.1 Summary
7.1.1 Summaries of Traditional Regressions
7.1.2 Summaries of Kernel Ridge Regressions
7.2 Introduction
7.3 Data Example
7.4 Traditional Linear Regression
7.5 Robust Regression
7.6 Quantile Regressions
7.7 Kernel Ridge Regression
7.8 Conclusion
7.8.1 Summaries of Traditional Regressions
7.8.2 Summaries of Kernel Ridge Regressions
7.9 References
Chapter 8: Effect of Gene Expressions on Drug Efficacy, 250 Patients, Traditional Regressions vs Kernel Ridge Regression
8.1 Summary
8.1.1 Summaries of Traditional Regressions
8.1.2 Summaries of Kernel Ridge Regressions
8.2 Introduction
8.3 Data Example
8.4 Traditional Linear Regression
8.5 Kernel Ridge Regression
8.6 Conclusion
8.6.1 Summaries of Traditional Regressions
8.6.2 Summaries of Kernel Ridge Regressions
8.7 References
Chapter 9: Effect of Gender, Treatment, and Their Interactions on Numbers of Paroxysmal Atrial Fibrillations, 40 Patients, Tra...
9.1 Summary
9.1.1 Summaries of Two Predictor Regressions
9.1.2 Summaries of Three Predictor Regressions
9.2 Introduction
9.3 Data Example
9.4 Traditional Linear Regression with Two Predictors
9.5 Kernel Ridge Regression with Two Predictors
9.6 Traditional Linear Regression with Three Predictors
9.7 Kernel Ridge Regression with Three Predictors
9.8 Conclusion
9.8.1 Summaries of Two Predictor Regressions
9.8.2 Summaries of Three Predictor Regressions
9.9 References
Chapter 10: Effect of Laboratory Predictors on Septic Mortality, 200 Patients, Traditional Regression vs Kernel Ridge Regressi...
10.1 Summary
10.1.1 Summaries of the Traditional Regressions
10.1.2 Summaries of the Kernel Ridge Regressions
10.2 Introduction
10.3 Data Example
10.4 Test and Retest Reliability
10.5 Binary Logistic Regression
10.6 Kernel Ridge Regression
10.7 Conclusion
10.7.1 Summaries of the Traditional Regressions
10.7.2 Summaries of the Kernel Ridge Regressions
10.8 References
Chapter 11: Effect of Month on Mean C-Reactive Protein, 18 Months, Traditional Regressions vs Kernel Ridge Regression
11.1 Summary
11.1.1 Summaries of Traditional Regressions
11.1.1.1 Autocorrelations
11.1.1.2 Curvilinear Regressions
11.1.2 Summaries of Kernel Ridge Regressions
11.2 Introduction
11.2.1 Summaries of Traditional Regressions
11.2.2 Summaries of Kernel Ridge Regressions
11.3 Autoregression Analysis
11.4 Data Example
11.5 Assessing Seasonality with Autocorrelations
11.6 Assessing Seasonality with Curvilinear Regressions
11.7 Assessing Seasonality with Kernel Ridge Regressions
11.8 Conclusions
11.8.1 Summaries of Traditional Regressions
11.8.2 Summaries of Kernel Ridge Regressions
11.9 References
Chapter 12: Effect of Different Dosages of Prednisone and Beta-Agonist on Peakflow, 78 Patients, Traditional Regressions vs Ke...
12.1 Summary
12.1.1 Summaries of Traditional Regressions
12.1.2 Summaries of Kernel Ridge Regressions
12.2 Introduction
12.3 Data Example (Var = Variable)
12.4 Regressions with Inconstant Variability
12.5 Traditional Linear Regression
12.6 Weighted Least Squares Regression
12.7 Kernel Ridge Regression
12.8 Conclusion
12.8.1 Summaries of Traditional Regressions
12.8.2 Summaries of Kernel Ridge Regressions
12.9 References
Chapter 13: Effect of Race, Age, and Gender on Physical Strength, 60 Patients, Traditional Regressions vs Kernel Ridge Regress...
13.1 Summary
13.1.1 Summaries of Traditional Regressions
13.1.2 Summaries of Kernel Ridge Regressions
13.2 Introduction
13.3 Data Example
13.4 Restructuring Traditional Multiple Variables Regression
13.5 Unrestructured Traditional Regression
13.6 Restructured Traditional Regression
13.7 Unrestructured Kernel Ridge Regressions with Race as Categorical Predictor Variable
13.8 Restructured Kernel Ridge Regressions
13.9 Conclusion
13.9.1 Summaries of Traditional Regressions
13.9.2 Summaries of Kernel Ridge Regressions
13.10 References
Chapter 14: Effect of Treatment, Age, Gender, and Co-morbidity on Hours of Sleep, 20 Patients, Traditional Regression vs Kerne...
14.1 Summary
14.1.1 Summaries of Traditional Linear Regressions
14.1.2 Summaries of Kernel Ridge Regressions
14.2 Introduction
14.3 Data Example
14.4 Traditional Regression
14.5 Kernel Ridge Regression
14.6 Conclusion
14.6.1 Summaries of Traditional Linear Regressions
14.6.2 Summaries of Kernel Ridge Regressions
14.7 References
Chapter 15: Effect of Counseling and Non-compliance on Monthly Stools, 35 Constipated Patients, Traditional Regressions vs Ker...
15.1 Summary
15.1.1 Summaries of Traditional Regressions
15.1.2 Summaries of Kernel Ridge Regressions
15.2 Introduction
15.3 Data Example
15.4 Traditional Regression Analysis
15.5 Two Stage Least Squares (2SLS)
15.6 Kernel Ridge Regression
15.7 Conclusion
15.7.1 Summaries of Traditional Regressions
15.7.2 Summaries of Kernel Ridge Regressions
15.8 References
Chapter 16: Effect of Treatment Modality, Counseling, and Satisfaction with Doctor on Quality of Life, 450 Patients, Tradition...
16.1 Summary
16.1.1 Summaries of Traditional Regressions
16.1.2 Summaries of Kernel Density Regressions
16.2 Introduction
16.3 Data Example
16.4 Traditional Linear Regression
16.5 Multinomial Regression
16.6 Ordinal Regression
16.7 Kernel Ridge Regression
16.8 Conclusions
16.8.1 Summaries of Traditional Regressions
16.8.2 Summaries of Kernel Density Regressions
16.9 References
Chapter 17: Effect of Department and Patient-Age on Risk of Falling out of Bed, 55 Patients, Traditional Regression vs Kernel ...
17.1 Summary
17.1.1 Summary of Traditional Regression with Multinomial Logistic Regression
17.1.2 Summary of Kernel Ridge Regression
17.2 Introduction
17.3 Data Example
17.4 Traditional Regression with Multinomial Logistic Regression
17.5 Kernel Ridge Regression
17.6 Conclusions
17.6.1 Traditional Regression with Multinomial Logistic Regression
17.6.2 Kernel Ridge Regressions
17.7 References
Chapter 18: Effect of Diet, Gender, Sport, and Medical Treatment on LDL Cholesterol Reduction, 953 Patients, Traditional Regre...
18.1 Summary
18.1.1 Summaries of the Traditional Multiple Variables Linear Regressions
18.1.2 Summaries of Kernel Ridge Regressions
18.2 Introduction
18.3 Data Example
18.4 Traditional Linear Regression
18.4.1 Decision Tree Analysis (Exhaustive Testing)
18.5 Kernel Ridge Regression
18.6 Conclusions
18.6.1 Summaries of the Traditional Multiple Variables Linear Regressions
18.6.2 Summaries of Kernel Ridge Regressions
18.7 References
Chapter 19: Effect of Gender, Age, Weight, and Height on Measured Body Surface, 90 Patients, Traditional Regression vs Kernel ...
19.1 Summary
19.1.1 Summaries of the Traditional Multiple Variables Linear Regression
19.1.2 Summaries of the Kernel Ridge Regression
19.2 Introduction
19.3 Data Example
19.4 Traditional Linear Regression
19.5 Kernel Ridge Regression
19.6 Conclusions
19.6.1 Summaries of the Traditional Multiple Variables Linear Regression
19.6.2 Summaries of the Kernel Ridge Regression
19.7 References
Chapter 20: Effect of Physicians´ Characteristics on Their Inclination to Give Lifestyle Advise or Not, 139 Physicians, Tradit...
20.1 Summary
20.1.1 Summaries of Traditional Multiple Variables Logistic Regressions
20.1.2 Summaries of Kernel Ridge Regressions
20.2 Introduction
20.3 Patient Example
20.4 Traditional Regression (Binary Logistic Regression)
20.5 Kernel Ridge Regression
20.6 Conclusion
20.6.1 Summaries of Traditional Multiple Variables Logistic Regressions
20.6.2 Summaries of Kernel Ridge Regressions
20.7 References
Chapter 21: Effect of Treatment, Psychological and Social Scores on Numbers of Paroxysmal Atrial Fibrillations, 50 Patients, T...
21.1 Summary
21.1.1 Summary of Traditional Regressions
21.1.2 Summary of Kernel Ridge Regressions
21.2 Introduction
21.3 Data Example
21.4 Traditional Linear Regressions
21.5 Kernel Ridge Regression
21.6 Conclusion
21.6.1 Summary of Traditional Regressions
21.6.2 Summary of Kernel Ridge Regressions
21.7 References
Chapter 22: Effect of Various Predictors on Numbers of Convulsions in 3390 Patients, Traditional vs Kernel Ridge Regression
22.1 Summary
22.1.1 Summaries of Traditional Multiple Variables Linear Regressions
22.1.2 Summaries of Kernel Ridge Linear Regressions
22.2 Introduction
22.3 Data Example
22.4 Traditional Multiple Variables Regressions
22.5 Kernel Ridge Regression
22.6 Conclusion
22.6.1 Summaries of Traditional Multiple Variables Linear Regressions
22.6.2 Summaries of Kernel Ridge Linear Regressions
22.7 References
Chapter 23: Effect of Foods Served on Breakfast Taken, 252 Persons, Traditional Linear and Multinomial Logistic Regression vs ...
23.1 Summary
23.1.1 Summaries of the Traditional Multiple Variables Linear Regression and Multinomial Logistic Regression
23.1.2 Summaries of the Kernel Ridge Regressions
23.2 Introduction
23.3 Data Example
23.4 Traditional Linear Regression and Multinomial Logistic Regression
23.5 Kernel Ridge Regressions
23.6 Conclusion
23.6.1 Summaries of the Traditional Multiple Variables Linear Regression
23.6.2 Summaries of the Kernel Ridge Regressions
23.7 References
Chapter 24: Effect of Personal Factors on Anorexia, 217 Persons, Traditional Linear Regression vs Kernel Ridge Regressions
24.1 Summary
24.1.1 Summaries of the Traditional Multiple Variables Linear Regression
24.1.2 Summaries of the Kernel Ridge Regression
24.2 Introduction
24.3 Data Example
24.4 Traditional Linear Regression
24.5 Kernel Ridge Regressions
24.6 Conclusions
24.6.1 Summaries of the Traditional Multiple Variables Linear Regression
24.6.2 Summaries of the Kernel Ridge Regression
24.7 References
Chapter 25: Effect on Weight Loss of Physical Exercise, Calorie Intake, Their Interaction, and Age, in 64 Patients, Traditiona...
25.1 Summary
25.1.1 Summaries of the Traditional Multiple Variables Linear Regression
25.1.2 Summaries of Kernel Ridge Regressions
25.2 Introduction
25.3 Data Example
25.4 Traditional Linear Regression
25.5 Kernel Ridge Regression
25.6 Conclusion
25.6.1 Summaries of the Traditional Multiple Variables Linear Regression
25.6.2 Summaries of Kernel Ridge Regressions
25.7 References
Chapter 26: Summaries
26.1 Chapter 1
26.2 Chapter 2
26.3 Chapter 3
26.3.1 Summaries of the Traditional Linear Regressions
26.3.2 Summaries of the Kernel Ridge Regressions
26.3.3 In Conclusion
26.4 Chapter 4
26.5 Chapter 5
26.6 Chapter 6
26.6.1 Summaries of Traditional Regressions
26.6.2 Summaries of Kernel Ridge Regressions
26.7 Chapter 7
26.7.1 Summaries of Traditional Regressions
26.7.2 Summaries of Kernel Ridge Regressions
26.8 Chapter 8
26.8.1 Summaries of Traditional Regressions
26.8.2 Summaries of Kernel Ridge Regressions
26.9 Chapter 9
26.9.1 Summaries of Two Predictor Regressions
26.9.2 Summaries of Three Predictor Regressions
26.10 Chapter 10
26.10.1 Summaries of the Traditional Regressions
26.10.2 Summaries of the Kernel Ridge Regressions
26.11 Chapter 11
26.11.1 Summaries of Traditional Regressions
26.11.1.1 Autocorrelations
26.11.1.2 Curvilinear Regressions
26.11.2 Summaries of Kernel Ridge Regressions
26.12 Chapter 12
26.12.1 Summaries of Traditional Regressions
26.12.2 Summaries of Kernel Ridge Regressions
26.13 Chapter 13
26.13.1 Summaries of Traditional Regressions
26.13.2 Summaries of Kernel Ridge Regressions
26.14 Chapter 14
26.14.1 Summaries of Traditional Linear Regressions
26.14.2 Summaries of Kernel Ridge Regressions
26.15 Chapter 15
26.15.1 Summaries of Traditional Linear Regressions
26.15.2 Summaries of Kernel Ridge Regressions
26.16 Chapter 16
26.16.1 Summaries of Traditional Regressions
26.16.2 Summaries of Kernel Density Regressions
26.17 Chapter 17
26.17.1 Summary of Traditional Regression with Multinomial Logistic Regression
26.17.2 Summary of Kernel Ridge Regression
26.18 Chapter 18
26.18.1 Summaries of the Traditional Multiple Variables Linear Regressions
26.18.2 Summaries of Kernel Ridge Regressions
26.19 Chapter 19
26.19.1 Summaries of the Traditional Multiple Variables Linear Regression
26.19.2 Summaries of the Kernel Ridge Regression
26.20 Chapter 20
26.20.1 Summaries of Traditional Multiple Variables Logistic Regressions
26.20.2 Summaries of Kernel Ridge Regressions
26.21 Chapter 21
26.21.1 Summary of Traditional Regressions
26.21.2 Summary of Kernel Ridge Regressions
26.22 Chapter 22
26.22.1 Summaries of Traditional Multiple Variables Linear Regressions
26.22.2 Summaries of Kernel Ridge Linear Regressions
26.23 Chapter 23
26.23.1 Summaries of the Traditional Multiple Variables Linear Regression and Multinomial Logistic Regression
26.23.2 Summaries of the Kernel Ridge Regressions
26.24 Chapter 24
26.24.1 Summaries of the Traditional Multiple Variables Linear Regression
26.24.2 Summaries of the Kernel Ridge Regression
26.25 Chapter 25
26.25.1 Summaries of the Traditional Multiple Variables Linear Regression
26.25.2 Summaries of Kernel Ridge Regressions
